The broad objective of this project is to develop an integrated wet-lab / dry-lab kit for high-throughput detection of RNA modifications. The final product will provide all required molecular biology reagents, a set of simple and robust experimental manipulations for facile analysis of modifications, and researcher-friendly analytic cloud-based software, requiring no special bioinformatics skills or computing hardware. Emerging data suggests that post-transcriptional modifications of many types of cellular RNA play an important and largely unexplored role in biology and in development and maintenance of the nervous system. In particular, a notable variety of neural diseases are due to specific defects in RNA modification, including autosomal recessive intellectual disability (ARID), non-syndromic X-linked mental retardation, myoclonus epilepsy ragged-red fibers (MERRF), mitochondrial encephalopathy lactic acidosis with stroke-like episodes (MELAS), and familial dysautonomia. Initial evidence for the links between RNA modification regulation and gene expression, mRNA splicing, nuclear export, and protein translation suggest much broader unknown effects given that modification states for most RNAs in most cell types are not well-defined. High-throughput RNA sequencing has revolutionized the study of gene expression, but it has not been widely applied to identifying most types of RNA modifications due to lack of methods for detection of modifications that are suitable for commercial use, and lack of methods for complex post-sequencing analytics. This Phase 1 proposal focuses on developing such methodology for three types of biologically relevant but relatively understudied RNA modifications, with the following Specific Aims: I. Develop methods for identifying RNA sites with m1A, m3C, or 2'-O-methylation. II. Develop an analysis pipeline to identify sites with 2'-O-methylation, m1A, or m3C. To achieve these aims, a novel biochemical approach will be employed for detecting and mapping 2'-O- methylation leveraging a known chemical reaction coupled with ligation methods to enrich for these sites. A distinct, novel approach will be applied to detect m1A, and m3C modifications, by differential analysis of reverse transcription between enzymatically treated samples. An integrated, cloud-based platform with custom analytic pipelines will be developed for processing and interpreting the RNA sequencing data to simplify complex analyses required to identify modifications with precision. Together, these all-inclusive kits will form both pre- and post-sequencing means for large-scale RNA modification detection with turn-key ease of use.